FROG makes full-resolution graph structure learnable in relational deep learning by modeling table roles as optimizable components in message passing, regularized by functional dependency constraints.
Integrating temporal and structural context in graph transformers for relational deep learning
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.LG 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
Proposes a pretrained Universal Row Encoder using transformers and global statistics to generate table-width invariant row embeddings for modular relational graph models, claiming improved transfer, convergence, and memory on RelBench.
GelGT proposes collaborative sampling and Gaussian attention on subgraphs to model long-range structural, semantic, and temporal dependencies in relational graphs, reporting up to 13.8% gains on downstream tasks.
citing papers explorer
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Is Fixing Schema Graphs Necessary? Full-Resolution Graph Structure Learning for Relational Deep Learning
FROG makes full-resolution graph structure learnable in relational deep learning by modeling table roles as optimizable components in message passing, regularized by functional dependency constraints.
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Universal Encoders for Modular Relational Deep Learning
Proposes a pretrained Universal Row Encoder using transformers and global statistics to generate table-width invariant row embeddings for modular relational graph models, claiming improved transfer, convergence, and memory on RelBench.
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Gaussian Relational Graph Transformer
GelGT proposes collaborative sampling and Gaussian attention on subgraphs to model long-range structural, semantic, and temporal dependencies in relational graphs, reporting up to 13.8% gains on downstream tasks.